✨ add flake8

  - pytest --cov=deepchem deepchem

  - bash devtools/run_doctest.sh

  - mypy -p deepchem

  - bash devtools/run_format_code.sh

  - bash devtools/run_flake8.sh

  - bash devtools/run_yapf.sh

  - bash devtools/run_docs_build.sh

after_success:

# flake8: noqa

from deepchem.hyper.base_classes import HyperparamOpt

from deepchem.hyper.grid_search import GridHyperparamOpt

from deepchem.hyper.gaussian_process import GaussianProcessHyperparamOpt

"""

Contains class for gaussian process hyperparameter optimizations.

"""

import os

import logging

import numpy as np

import tempfile

import os

import deepchem

from deepchem.hyper.base_classes import HyperparamOpt

from deepchem.utils.evaluate import Evaluator

from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename

logger = logging.getLogger(__name__)

    ########################

    import pyGPGO

    from pyGPGO.covfunc import matern32

    from pyGPGO.acquisition import Acquisition

    from pyGPGO.surrogates.GaussianProcess import GaussianProcess

process optimizer doesn't find an optimal point. This is still a

valuable test suite so leaving it in despite the flakiness.

"""

import os

import numpy as np

import sklearn

import deepchem as dc

"""

Tests for hyperparam optimization.

"""

import os

import unittest

import tempfile

import shutil

import numpy as np

import tensorflow as tf

import deepchem as dc

import sklearn

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import RandomForestRegressor

class TestGridHyperparamOpt(unittest.TestCase):

            n_features=3,

            dropouts=[0.],

            weight_init_stddevs=[np.sqrt(6) / np.sqrt(1000)],

            #learning_rate=0.003, **p))

            **p))

    params_dict = {"learning_rate": [0.003, 0.03], "batch_size": [10, 50]}